please assign the issue to yourself in github so people know someone is working on it.
It doesn't look like users can assign issues to themselves without being invited to be a contributor.
Now that I have a copy of the code running on my local machine, I was thinking of grabbing an issue to work on. (I can't promise any commitment level beyond one issue yet.) I'm trying to be thorough reading what docs there are, and I've come across the contributing guidelines which says to check out a roadmap (a Trello board) and join a Slack channel before working on anything. The Trello board doesn't make much sense to me and I'm not sure if either of these instructions are still important to follow, or if I really should just claim an unassigned issue with a 'good first issue' or 'help wanted' tag.
Adding the missing line fixed it! I have Lesswrong2 running successfully on Windows at http://localhost:3000/ now.
This might be a good use case for someone to create a Docker image (or some other container) that has a development environment that just works for new users.
If you try again, I think you can avoid needing bash. See my comment here.
I got further than gjm reported.
I needed to:
I learned that npm start is unnecessary. It runs a bash script that
1. checks meteor is installed
2. creates the file settings.json if it doesn't exist by copying from sample_settings.json
3. runs command meteor --settings settings.json
If you have meteor installed and create settings.json yourself, you can skip npm start and just run
meteor --settings settings.json
I verified that I was able to... (read more)
Your comment was definitely worthwhile for me. Thanks to your very strong recommendation (and the fact that it doesn't look like it'll take much time), I'm going to check out the fast.ai course very soon. I'll be referencing back to this comment to check out your other recommendations in the future too. Thank you.
I use Windows and intend to try to get this running on my machine.
Also, fyi, the first link to the GitHub repo in the tl;dr doesn't point to the right place.
It's always a bit amazing to me how much I don't have to remember to be able to work on big software projects. It's like as long as I know what's possible, and when it's applicable, it takes only moments to search for and zero in on specific implementation details.
And yet in this situation, some anxious voice in my head cries, "But do you really know what you're doing if you can't remember every detail?!"
So thank you for reassurance on that. Also, thank you for the recommendations!
I recently finished the Machine Learning course on Coursera that is recommended by MIRI's research guide for developing a practical familiarity with machine learning.
This post contains my thoughts about the course and tries to convey the updates my mental models went through as the eleven week course progressed.
I started the course with the perspective of an experienced, employed software developer with an engineering degree who had never focused on machine learning before, so I'm sure there's some background knowledge I take for granted, as well as some things I should have realized long before taking this course.
A definition introduced early in the lectures defines machine learning... (read 1713 more words →)
Somewhere, recently, I saw someone comment almost in passing that grad school shouldn't cost anything. I can't find the source now. Maybe someone can clarify if that's a serious claim? I've been under the impression for a while that grad school and academia would be an awfully expensive way to acquire the prerequisite knowledge for AI safety work.